{"id":"W4386474156","doi":"10.1109/lcn58197.2023.10223377","title":"Towards Energy Efficiency in RAN Network Slicing","year":2023,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nature; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Slicing; Quality of service; Computer science; Energy consumption; Efficient energy use; Computer network; Base station; Energy (signal processing); Service (business); Work (physics); Distributed computing; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004326424,0.0001153639,0.0001562179,0.0001403011,0.00009208907,0.00009598647,0.0006702305,0.00006079117,0.00002801648],"category_scores_gemma":[0.0000355872,0.00009771321,0.00005320464,0.0025759,0.00001691911,0.0001592466,0.0003346316,0.0000921148,0.00009687804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002567803,"about_ca_system_score_gemma":0.00004761592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004529417,"about_ca_topic_score_gemma":0.0001849741,"domain_scores_codex":[0.9986367,0.00004552661,0.000211263,0.0003478967,0.0002187881,0.0005397894],"domain_scores_gemma":[0.9992864,0.0001878492,0.00003118876,0.0003981422,0.00002133016,0.00007504927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006068065,0.00004751465,0.007348464,0.000007587282,0.000009038476,0.0001117273,0.0006583178,0.1597032,0.00002706446,0.4460734,0.04823153,0.3377761],"study_design_scores_gemma":[0.000456059,0.00006434143,0.01767166,0.00004313861,0.000001731798,0.000007280491,0.00002599551,0.9474907,0.0001041675,0.01491242,0.01892064,0.0003018826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01590667,0.0001908109,0.9597277,0.001218121,0.001022256,0.00006734406,2.095799e-7,0.001173218,0.02069364],"genre_scores_gemma":[0.9869283,0.00009922057,0.009885843,0.001468044,0.0002927732,0.00001776979,0.000002675688,0.00001312834,0.001292248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9710217,"threshold_uncertainty_score":0.3984629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587357276177394,"score_gpt":0.2342043666366224,"score_spread":0.2183307938748484,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}